Overview

Dataset statistics

Number of variables13
Number of observations61
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory138.5 B

Variable types

Text1
Numeric12

Alerts

CochesVendidos_2017 is highly overall correlated with CochesVendidos_2018 and 4 other fieldsHigh correlation
CochesVendidos_2018 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2019 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2020 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2021 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
CochesVendidos_2022 is highly overall correlated with CochesVendidos_2017 and 4 other fieldsHigh correlation
PIB_2017 is highly overall correlated with PIB_2018 and 4 other fieldsHigh correlation
PIB_2018 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2019 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2020 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2021 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
PIB_2022 is highly overall correlated with PIB_2017 and 4 other fieldsHigh correlation
Country has unique valuesUnique
PIB_2017 has unique valuesUnique
PIB_2018 has unique valuesUnique
PIB_2019 has unique valuesUnique
PIB_2020 has unique valuesUnique
PIB_2021 has unique valuesUnique
PIB_2022 has unique valuesUnique
CochesVendidos_2017 has unique valuesUnique
CochesVendidos_2018 has unique valuesUnique
CochesVendidos_2019 has unique valuesUnique
CochesVendidos_2020 has unique valuesUnique
CochesVendidos_2021 has unique valuesUnique
CochesVendidos_2022 has unique valuesUnique

Reproduction

Analysis started2023-11-25 16:59:07.926665
Analysis finished2023-11-25 16:59:24.878204
Duration16.95 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Country
Text

UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-11-25T17:59:25.023060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.295082
Min length2

Characters and Unicode

Total characters445
Distinct characters47
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)100.0%

Sample

1st rowArgentina
2nd rowAustralia
3rd rowAustria
4th rowBelgium
5th rowBrazil
ValueCountFrequency (%)
argentina 1
 
1.5%
egypt 1
 
1.5%
australia 1
 
1.5%
luxembourg 1
 
1.5%
austria 1
 
1.5%
belgium 1
 
1.5%
brazil 1
 
1.5%
bulgaria 1
 
1.5%
canada 1
 
1.5%
chile 1
 
1.5%
Other values (56) 56
84.8%
2023-11-25T17:59:25.389601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 67
15.1%
i 38
 
8.5%
n 35
 
7.9%
e 31
 
7.0%
r 26
 
5.8%
l 21
 
4.7%
o 18
 
4.0%
t 17
 
3.8%
u 16
 
3.6%
s 13
 
2.9%
Other values (37) 163
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 369
82.9%
Uppercase Letter 71
 
16.0%
Space Separator 5
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 67
18.2%
i 38
10.3%
n 35
9.5%
e 31
 
8.4%
r 26
 
7.0%
l 21
 
5.7%
o 18
 
4.9%
t 17
 
4.6%
u 16
 
4.3%
s 13
 
3.5%
Other values (14) 87
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 9
12.7%
A 7
 
9.9%
U 6
 
8.5%
C 6
 
8.5%
I 5
 
7.0%
P 5
 
7.0%
R 4
 
5.6%
K 4
 
5.6%
M 3
 
4.2%
N 3
 
4.2%
Other values (12) 19
26.8%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 440
98.9%
Common 5
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 67
15.2%
i 38
 
8.6%
n 35
 
8.0%
e 31
 
7.0%
r 26
 
5.9%
l 21
 
4.8%
o 18
 
4.1%
t 17
 
3.9%
u 16
 
3.6%
s 13
 
3.0%
Other values (36) 158
35.9%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 67
15.1%
i 38
 
8.5%
n 35
 
7.9%
e 31
 
7.0%
r 26
 
5.8%
l 21
 
4.7%
o 18
 
4.0%
t 17
 
3.8%
u 16
 
3.6%
s 13
 
2.9%
Other values (37) 163
36.6%

PIB_2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27826.312
Minimum1177.083
Maximum111211.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.0 B
2023-11-25T17:59:25.562644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1177.083
5-th percentile1957.97
Q19599.362
median20605.825
Q342275.793
95-th percentile69864.671
Maximum111211.77
Range110034.69
Interquartile range (IQR)32676.431

Descriptive statistics

Standard deviation23402.728
Coefficient of variation (CV)0.84102873
Kurtosis1.5284589
Mean27826.312
Median Absolute Deviation (MAD)14028.538
Skewness1.1761968
Sum1697405
Variance5.4768765 × 108
MonotonicityNot monotonic
2023-11-25T17:59:25.720993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14618.327 1
 
1.6%
9599.362 1
 
1.6%
48799.874 1
 
1.6%
42275.793 1
 
1.6%
75940.152 1
 
1.6%
17731.849 1
 
1.6%
1653.406 1
 
1.6%
3153.314 1
 
1.6%
13819.834 1
 
1.6%
21482.834 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
1177.083 1
1.6%
1653.406 1
1.6%
1932.763 1
1.6%
1957.97 1
1.6%
2592.707 1
1.6%
2655.99 1
1.6%
2957.899 1
1.6%
3153.314 1
1.6%
3885.465 1
1.6%
6577.287 1
1.6%
ValueCountFrequency (%)
111211.77 1
1.6%
82584.384 1
1.6%
75940.152 1
1.6%
69864.671 1
1.6%
61164.897 1
1.6%
59878.72 1
1.6%
57772.553 1
1.6%
55804.163 1
1.6%
53459.072 1
1.6%
48799.874 1
1.6%

PIB_2018
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29686.301
Minimum1271.677
Maximum117993.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.0 B
2023-11-25T17:59:25.874961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1271.677
5-th percentile1974.378
Q19848.949
median23380.772
Q345591.68
95-th percentile79184.608
Maximum117993.37
Range116721.69
Interquartile range (IQR)35742.731

Descriptive statistics

Standard deviation24924.645
Coefficient of variation (CV)0.83960092
Kurtosis1.5087854
Mean29686.301
Median Absolute Deviation (MAD)16457.132
Skewness1.1734538
Sum1810864.3
Variance6.2123794 × 108
MonotonicityNot monotonic
2023-11-25T17:59:26.037527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11786.433 1
 
1.6%
10024.115 1
 
1.6%
53224.694 1
 
1.6%
42761.93 1
 
1.6%
82605.966 1
 
1.6%
19885.103 1
 
1.6%
1698.034 1
 
1.6%
3279.519 1
 
1.6%
15504.366 1
 
1.6%
23573.299 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
1271.677 1
1.6%
1618.967 1
1.6%
1698.034 1
1.6%
1974.378 1
1.6%
2710.158 1
1.6%
3118.258 1
1.6%
3216.254 1
1.6%
3279.519 1
1.6%
3947.248 1
1.6%
6923.64 1
1.6%
ValueCountFrequency (%)
117993.371 1
1.6%
85546.669 1
1.6%
82605.966 1
1.6%
79184.608 1
1.6%
66836.539 1
1.6%
62787.782 1
1.6%
61724.492 1
1.6%
56352.938 1
1.6%
54295.731 1
1.6%
53224.694 1
1.6%

PIB_2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29249.866
Minimum1302.277
Maximum113860.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.0 B
2023-11-25T17:59:26.197041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1302.277
5-th percentile2050.163
Q110054.023
median23584.26
Q343981.605
95-th percentile76303.683
Maximum113860.53
Range112558.26
Interquartile range (IQR)33927.582

Descriptive statistics

Standard deviation24296.099
Coefficient of variation (CV)0.83063966
Kurtosis1.339323
Mean29249.866
Median Absolute Deviation (MAD)16963.707
Skewness1.1397306
Sum1784241.9
Variance5.9030044 × 108
MonotonicityNot monotonic
2023-11-25T17:59:26.359192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10054.023 1
 
1.6%
10311.552 1
 
1.6%
52672.504 1
 
1.6%
42287.718 1
 
1.6%
76303.683 1
 
1.6%
19069.311 1
 
1.6%
1500.683 1
 
1.6%
3512.195 1
 
1.6%
15694.594 1
 
1.6%
23333.317 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
1302.277 1
1.6%
1500.683 1
1.6%
1812.738 1
1.6%
2050.163 1
1.6%
3240.513 1
1.6%
3439.102 1
1.6%
3512.195 1
1.6%
3688.953 1
1.6%
4194.086 1
1.6%
6540.141 1
1.6%
ValueCountFrequency (%)
113860.533 1
1.6%
84474.469 1
1.6%
80613.712 1
1.6%
76303.683 1
1.6%
66070.471 1
1.6%
65077.295 1
1.6%
59678.596 1
1.6%
54289.066 1
1.6%
52672.504 1
1.6%
51694.498 1
1.6%

PIB_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28383.234
Minimum1376.512
Maximum117616.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.0 B
2023-11-25T17:59:26.509503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1376.512
5-th percentile1913.22
Q19062.958
median22682.901
Q343383.713
95-th percentile68275.277
Maximum117616.15
Range116239.64
Interquartile range (IQR)34320.755

Descriptive statistics

Standard deviation24479.492
Coefficient of variation (CV)0.86246308
Kurtosis1.9495544
Mean28383.234
Median Absolute Deviation (MAD)17010.623
Skewness1.2867883
Sum1731377.3
Variance5.9924551 × 108
MonotonicityNot monotonic
2023-11-25T17:59:26.804786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8571.937 1
 
1.6%
8770.022 1
 
1.6%
52222.364 1
 
1.6%
41307.21 1
 
1.6%
68275.277 1
 
1.6%
17076.319 1
 
1.6%
1376.512 1
 
1.6%
3325.836 1
 
1.6%
15792.61 1
 
1.6%
22224.555 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
1376.512 1
1.6%
1527.434 1
1.6%
1776.267 1
1.6%
1913.22 1
1.6%
3325.836 1
1.6%
3548.892 1
1.6%
3780.075 1
1.6%
3802.438 1
1.6%
3932.332 1
1.6%
5363.067 1
1.6%
ValueCountFrequency (%)
117616.151 1
1.6%
86109.53 1
1.6%
85786.688 1
1.6%
68275.277 1
1.6%
63577.341 1
1.6%
61274.006 1
1.6%
60926.877 1
1.6%
53094.491 1
1.6%
52706.294 1
1.6%
52222.364 1
1.6%

PIB_2021
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32696.998
Minimum1216.811
Maximum134925.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.0 B
2023-11-25T17:59:26.953377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1216.811
5-th percentile2238.127
Q110631.665
median26794.625
Q351460.987
95-th percentile90540.804
Maximum134925.16
Range133708.35
Interquartile range (IQR)40829.322

Descriptive statistics

Standard deviation28424.87
Coefficient of variation (CV)0.86934187
Kurtosis1.8932183
Mean32696.998
Median Absolute Deviation (MAD)18628.988
Skewness1.308099
Sum1994516.9
Variance8.0797321 × 108
MonotonicityNot monotonic
2023-11-25T17:59:27.120695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10631.665 1
 
1.6%
10177.039 1
 
1.6%
58960.71 1
 
1.6%
48792.618 1
 
1.6%
90540.804 1
 
1.6%
19479.401 1
 
1.6%
1565.575 1
 
1.6%
3576.11 1
 
1.6%
18008.152 1
 
1.6%
24703.713 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
1216.811 1
1.6%
1565.575 1
1.6%
2013.971 1
1.6%
2238.127 1
1.6%
3576.11 1
1.6%
3753.428 1
1.6%
4145.939 1
1.6%
4362.677 1
1.6%
4874.309 1
1.6%
6239.269 1
1.6%
ValueCountFrequency (%)
134925.164 1
1.6%
101983.636 1
1.6%
93700.472 1
1.6%
90540.804 1
1.6%
77710.07 1
1.6%
70159.774 1
1.6%
69466.552 1
1.6%
63841.724 1
1.6%
61203.119 1
1.6%
58960.71 1
1.6%

PIB_2022
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33313.965
Minimum1227.697
Maximum126598.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size620.0 B
2023-11-25T17:59:27.284708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1227.697
5-th percentile2391.866
Q112465.612
median26832.304
Q349843.163
95-th percentile93657.234
Maximum126598.1
Range125370.41
Interquartile range (IQR)37377.551

Descriptive statistics

Standard deviation28513.188
Coefficient of variation (CV)0.85589297
Kurtosis1.442785
Mean33313.965
Median Absolute Deviation (MAD)18628.762
Skewness1.266142
Sum2032151.8
Variance8.1300189 × 108
MonotonicityNot monotonic
2023-11-25T17:59:27.452584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13619.875 1
 
1.6%
11265.544 1
 
1.6%
57427.756 1
 
1.6%
47226.095 1
 
1.6%
105825.926 1
 
1.6%
23240.138 1
 
1.6%
1650.279 1
 
1.6%
3623.593 1
 
1.6%
18342.676 1
 
1.6%
24540.377 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
1227.697 1
1.6%
1650.279 1
1.6%
2279.972 1
1.6%
2391.866 1
1.6%
3623.593 1
1.6%
4086.519 1
1.6%
4587.172 1
1.6%
4606.798 1
1.6%
4798.118 1
1.6%
6658.119 1
1.6%
ValueCountFrequency (%)
126598.103 1
1.6%
105825.926 1
1.6%
103311.007 1
1.6%
93657.234 1
1.6%
82807.649 1
1.6%
76343.247 1
1.6%
68294.907 1
1.6%
64813.854 1
1.6%
57427.756 1
1.6%
56188.324 1
1.6%

CochesVendidos_2017
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159222.7
Minimum7801
Maximum16188680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-11-25T17:59:27.609163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7801
5-th percentile27040
Q1118879
median273458
Q3979039
95-th percentile3419716
Maximum16188680
Range16180879
Interquartile range (IQR)860160

Descriptive statistics

Standard deviation2777559.7
Coefficient of variation (CV)2.3960535
Kurtosis21.452898
Mean1159222.7
Median Absolute Deviation (MAD)192969
Skewness4.5111387
Sum70712587
Variance7.7148381 × 1012
MonotonicityNot monotonic
2023-11-25T17:59:27.780705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
859770 1
 
1.6%
1508843 1
 
1.6%
386442 1
 
1.6%
145119 1
 
1.6%
177900 1
 
1.6%
140132 1
 
1.6%
239725 1
 
1.6%
434691 1
 
1.6%
472921 1
 
1.6%
243360 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
7801 1
1.6%
24223 1
1.6%
24834 1
1.6%
27040 1
1.6%
44264 1
1.6%
50165 1
1.6%
50495 1
1.6%
70304 1
1.6%
80489 1
1.6%
83991 1
1.6%
ValueCountFrequency (%)
16188680 1
1.6%
14117684 1
1.6%
4973577 1
1.6%
3419716 1
1.6%
2800661 1
1.6%
2630610 1
1.6%
2439778 1
1.6%
2169110 1
1.6%
2037877 1
1.6%
1876296 1
1.6%

CochesVendidos_2018
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1167537.2
Minimum16791
Maximum16263975
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-11-25T17:59:27.948215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16791
5-th percentile37380
Q1125618
median262000
Q31012499
95-th percentile3428367
Maximum16263975
Range16247184
Interquartile range (IQR)886881

Descriptive statistics

Standard deviation2771461.8
Coefficient of variation (CV)2.3737674
Kurtosis21.411888
Mean1167537.2
Median Absolute Deviation (MAD)173453
Skewness4.4969711
Sum71219772
Variance7.6810006 × 1012
MonotonicityNot monotonic
2023-11-25T17:59:28.120151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
772980 1
 
1.6%
1396939 1
 
1.6%
410003 1
 
1.6%
146197 1
 
1.6%
168174 1
 
1.6%
122849 1
 
1.6%
254560 1
 
1.6%
378354 1
 
1.6%
514889 1
 
1.6%
251235 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
16791 1
1.6%
25581 1
1.6%
30218 1
1.6%
37380 1
1.6%
41650 1
1.6%
52426 1
1.6%
58557 1
1.6%
72013 1
1.6%
82917 1
1.6%
88547 1
1.6%
ValueCountFrequency (%)
16263975 1
1.6%
13919656 1
1.6%
5014383 1
1.6%
3428367 1
1.6%
2928332 1
1.6%
2519080 1
1.6%
2475994 1
1.6%
2458022 1
1.6%
1971108 1
1.6%
1832162 1
1.6%

CochesVendidos_2019
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1137015.1
Minimum20764
Maximum15956729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-11-25T17:59:28.286437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20764
5-th percentile38328
Q1117691
median262564
Q3934137
95-th percentile3593854
Maximum15956729
Range15935965
Interquartile range (IQR)816446

Descriptive statistics

Standard deviation2687430.6
Coefficient of variation (CV)2.3635839
Kurtosis21.431032
Mean1137015.1
Median Absolute Deviation (MAD)174230
Skewness4.4834315
Sum69357920
Variance7.222283 × 1012
MonotonicityNot monotonic
2023-11-25T17:59:28.458224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
436794 1
 
1.6%
1288922 1
 
1.6%
415736 1
 
1.6%
139354 1
 
1.6%
162173 1
 
1.6%
108257 1
 
1.6%
186588 1
 
1.6%
387845 1
 
1.6%
537814 1
 
1.6%
245511 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
20764 1
1.6%
26839 1
1.6%
31726 1
1.6%
38328 1
1.6%
47274 1
1.6%
53656 1
1.6%
61707 1
1.6%
71850 1
1.6%
81875 1
1.6%
90908 1
1.6%
ValueCountFrequency (%)
15956729 1
1.6%
13188211 1
1.6%
4934557 1
1.6%
3593854 1
1.6%
2679239 1
1.6%
2577447 1
1.6%
2570268 1
1.6%
2410472 1
1.6%
1966372 1
1.6%
1772639 1
1.6%

CochesVendidos_2020
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean949651.52
Minimum15544
Maximum13602914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-11-25T17:59:28.628251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum15544
5-th percentile32477
Q199389
median215275
Q3720029
95-th percentile2926093
Maximum13602914
Range13587370
Interquartile range (IQR)620640

Descriptive statistics

Standard deviation2380262.6
Coefficient of variation (CV)2.506459
Kurtosis21.585784
Mean949651.52
Median Absolute Deviation (MAD)138788
Skewness4.5551141
Sum57928743
Variance5.6656502 × 1012
MonotonicityNot monotonic
2023-11-25T17:59:28.804768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
324340 1
 
1.6%
927577 1
 
1.6%
322283 1
 
1.6%
106059 1
 
1.6%
151411 1
 
1.6%
69534 1
 
1.6%
123403 1
 
1.6%
226673 1
 
1.6%
412650 1
 
1.6%
163364 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
15544 1
1.6%
18697 1
1.6%
21667 1
1.6%
32477 1
1.6%
35264 1
1.6%
45323 1
1.6%
50892 1
1.6%
52553 1
1.6%
58136 1
1.6%
69534 1
1.6%
ValueCountFrequency (%)
13602914 1
1.6%
12419372 1
1.6%
4375857 1
1.6%
2926093 1
1.6%
2116932 1
1.6%
1963614 1
1.6%
1963496 1
1.6%
1714894 1
1.6%
1586302 1
1.6%
1450789 1
1.6%

CochesVendidos_2021
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean972641.93
Minimum7673
Maximum14101851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-11-25T17:59:28.962763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7673
5-th percentile44019
Q1106380
median236044
Q3770439
95-th percentile2627208
Maximum14101851
Range14094178
Interquartile range (IQR)664059

Descriptive statistics

Standard deviation2394686.4
Coefficient of variation (CV)2.4620432
Kurtosis22.213995
Mean972641.93
Median Absolute Deviation (MAD)154735
Skewness4.6060294
Sum59331158
Variance5.7345231 × 1012
MonotonicityNot monotonic
2023-11-25T17:59:29.131673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
352514 1
 
1.6%
970626 1
 
1.6%
288920 1
 
1.6%
141467 1
 
1.6%
182274 1
 
1.6%
66356 1
 
1.6%
236044 1
 
1.6%
261666 1
 
1.6%
429918 1
 
1.6%
165172 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
7673 1
1.6%
21860 1
1.6%
23612 1
1.6%
44019 1
1.6%
44954 1
1.6%
46723 1
1.6%
52978 1
1.6%
53079 1
1.6%
66356 1
1.6%
81309 1
1.6%
ValueCountFrequency (%)
14101851 1
1.6%
12118025 1
1.6%
4232790 1
1.6%
2627208 1
1.6%
2537021 1
1.6%
1985907 1
1.6%
1982758 1
1.6%
1776985 1
1.6%
1533662 1
1.6%
1525872 1
1.6%

CochesVendidos_2022
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct61
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean949283.93
Minimum5118
Maximum13105950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size376.0 B
2023-11-25T17:59:29.295979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile36303
Q1106857
median236269
Q3775589
95-th percentile2914485
Maximum13105950
Range13100832
Interquartile range (IQR)668732

Descriptive statistics

Standard deviation2372786.1
Coefficient of variation (CV)2.4995537
Kurtosis21.656274
Mean949283.93
Median Absolute Deviation (MAD)152747
Skewness4.5737591
Sum57906320
Variance5.630114 × 1012
MonotonicityNot monotonic
2023-11-25T17:59:29.470559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
378633 1
 
1.6%
994945 1
 
1.6%
279093 1
 
1.6%
140207 1
 
1.6%
172307 1
 
1.6%
56723 1
 
1.6%
224617 1
 
1.6%
329205 1
 
1.6%
400428 1
 
1.6%
169705 1
 
1.6%
Other values (51) 51
83.6%
ValueCountFrequency (%)
5118 1
1.6%
23359 1
1.6%
27717 1
1.6%
36303 1
1.6%
37439 1
1.6%
41451 1
1.6%
42369 1
1.6%
45371 1
1.6%
49284 1
1.6%
56723 1
1.6%
ValueCountFrequency (%)
13105950 1
1.6%
12947827 1
1.6%
4027746 1
1.6%
2914485 1
1.6%
2618944 1
1.6%
1971613 1
1.6%
1774157 1
1.6%
1673864 1
1.6%
1402136 1
1.6%
1386382 1
1.6%

Interactions

2023-11-25T17:59:23.276689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.085752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.237869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.588006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.833549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.123254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.447731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.866843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.267238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.701003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.372006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:21.994164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.374427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.174544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.337245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.679761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.932352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.222433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.546444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.971127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.375493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.817730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.488235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.102000image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.473164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.267289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.430815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.780514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.033823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.327111image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.646199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.082048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.490494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.935490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.610987image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.204728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.570902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.356041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.519214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.873752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.132664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.430323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.745083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.190757image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.597953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:19.047192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.721692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.304461image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.668429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.447870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.735872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.974437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.234327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.538538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.846254image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.305905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.710032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:19.164020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.839381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.410219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.775359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.542871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.830910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.074412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.337143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.644949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.954364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.418063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.828733image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:19.312204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.964742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.522791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.873171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.631546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.924787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.174306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.440123image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.750202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.055347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.529718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.945703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:19.435880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:21.085714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.627466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.984528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.736265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.036556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.288999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.551824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.868752image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.172367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.649687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.073419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:19.579049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:21.260207image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.738808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:24.095225image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.837985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.155871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.400156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.671212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.989429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.288651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.769557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.200584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:19.808397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:21.390641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.850143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:24.205929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:08.942322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.267606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.511786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.787096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.110826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.405379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:16.912174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.328562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:19.967519image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:21.511334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:22.960760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:24.311781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.038847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.376610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.621492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:12.904868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.224081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.653677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.033487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.453815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.107164image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:21.622401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.066814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:24.415679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:09.139249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:10.485276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:11.729689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:13.015481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:14.336784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:15.760713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:17.153585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:18.579370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:20.242554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:21.880666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-25T17:59:23.173767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-25T17:59:29.587247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
CochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022PIB_2017PIB_2018PIB_2019PIB_2020PIB_2021PIB_2022
CochesVendidos_20171.0000.9930.9820.9630.9700.9660.1510.1520.1380.1250.1250.102
CochesVendidos_20180.9931.0000.9920.9780.9830.9780.0910.0920.0780.0690.0670.041
CochesVendidos_20190.9820.9921.0000.9910.9840.9780.0880.0890.0770.0670.0660.041
CochesVendidos_20200.9630.9780.9911.0000.9830.9690.0760.0760.0650.0600.0580.032
CochesVendidos_20210.9700.9830.9840.9831.0000.9810.0520.0510.0400.0340.0330.009
CochesVendidos_20220.9660.9780.9780.9690.9811.0000.0350.0350.0210.0140.010-0.011
PIB_20170.1510.0910.0880.0760.0520.0351.0000.9970.9950.9900.9920.989
PIB_20180.1520.0920.0890.0760.0510.0350.9971.0000.9980.9930.9940.991
PIB_20190.1380.0780.0770.0650.0400.0210.9950.9981.0000.9960.9970.994
PIB_20200.1250.0690.0670.0600.0340.0140.9900.9930.9961.0000.9980.990
PIB_20210.1250.0670.0660.0580.0330.0100.9920.9940.9970.9981.0000.994
PIB_20220.1020.0410.0410.0320.009-0.0110.9890.9910.9940.9900.9941.000

Missing values

2023-11-25T17:59:24.562324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-25T17:59:24.784502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryPIB_2017PIB_2018PIB_2019PIB_2020PIB_2021PIB_2022CochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022
0Argentina14618.32711786.43310054.0238571.93710631.66513619.875859770772980436794324340352514378633
1Australia55804.16356352.93854289.06653094.49163841.72464813.85411220731076319983031832657909983918373
2Austria47320.53751234.47750195.32148857.08353528.70752191.771379184370315358175274619286070226794
3Belgium44274.07347685.34746783.00545545.23351472.10349843.163578146579262584100464683416309385225
4Brazil10419.5769629.6039364.2377344.5298165.6379455.328216911024759942679239196349619827581971613
5Bulgaria8414.3899489.6579914.38610170.72312300.18513821.205242233021831726216672361227717
6Canada45191.99346625.85946449.96243383.71352387.81255036.520187629618321621772639141906815258721386382
7Chile15003.77015755.00314567.98813067.74216092.14915166.472317643356720310755207331292132284240
8China8760.2599848.94910170.06110525.00112572.07112669.617141176841391965613188211124193721211802513105950
9Colombia6577.2876923.6406540.1415363.0676239.2696658.119221987239593243742171191228549236269
CountryPIB_2017PIB_2018PIB_2019PIB_2020PIB_2021PIB_2022CochesVendidos_2017CochesVendidos_2018CochesVendidos_2019CochesVendidos_2020CochesVendidos_2021CochesVendidos_2022
51Switzerland82584.38485546.66984474.46986109.53093700.47293657.234323045309841324087250457254218240093
52Taiwan25061.62425825.56625903.16828571.44033186.34132687.371229128212677201192215275225785216911
53Thailand6593.8167298.9417812.8867169.8777227.4767069.589816464923253934137720029670160775589
54Ukraine2655.9903118.2583688.9533780.0754874.3094606.7988048982917909088709010358236303
55UAE41971.75345591.68043981.60537648.96343438.52351399.958253602206731205140138923179358197830
56UK40666.83143377.75942797.25840347.36446421.61145461.066263061024580222410472171489417769851673864
57USA59878.72062787.78265077.29563577.34170159.77476343.247161886801626397515956729136029141410185112947827
58Uruguay18626.98118604.95817636.70315198.36817333.60820022.144501654165038328324774672349284
59Uzbekistan1932.7631618.9671812.7381776.2672013.9712279.972118879203695267952276378216726216726
60Vietnam2957.8993216.2543439.1023548.8923753.4284086.519208320250255284376264569256840328862